19 papers with code • 4 benchmarks • 4 datasets
Keyword extraction is tasked with the automatic identification of terms that best describe the subject of a document (Source: Wikipedia).
Combination of the proposed graph construction and scoring methods leads to a novel, parameterless keyword extraction method (sCAKE) based on semantic connectivity of words in the document.
We present a fully unsupervised, extractive text summarization system that leverages a submodularity framework introduced by past research.
In this paper, we present YAKE!, a novel feature-based system for multi-lingual keyword extraction from single documents, which supports texts of different sizes, domains or languages.
Corpus2graph is an open-source NLP-application-oriented tool that generates a word co-occurrence network from a large corpus.
Keyword extraction is used for summarizing the content of a document and supports efficient document retrieval, and is as such an indispensable part of modern text-based systems.
Keyword extraction has received an increasing attention as an important research topic which can lead to have advancements in diverse applications such as document context categorization, text indexing and document classification.
With growing amounts of available textual data, development of algorithms capable of automatic analysis, categorization and summarization of these data has become a necessity.
Keyword extraction is an important document process that aims at finding a small set of terms that concisely describe a document's topics.